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Local population dynamics of an invasive tree species with a complex life-history cycle: A stochastic matrix model
Institution:1. Département de Botanique, Université de Picardie Jules Verne, F-80037 Amiens Cedex, France;2. Laboratoire Amiénois de Mathématiques Fondamentales et Appliquées (UMR 6140 CNRS), Université de Picardie Jules Verne, F-80037 Amiens Cedex, France;1. Department of Mathematics, Babe?-Bolyai University, Kog?lniceanu 1, 400084 Cluj-Napoca, Romania;2. Departament de Matemàtica, Universitat de Lleida, Avda. Jaume II, 69, 25001 Lleida, Spain;1. Departamento Matemática Aplicada I, Universidade de Vigo, E. E. Forestal, Campus Universitario A Xunqueira, 36005 Pontevedra, Spain;2. Departamento de Matemáticas, University of Santiago de Compostela, 15782, Spain;3. Institute of Mathematics, 81, Mirzo Ulug''bek str., 100170, Tashkent, Uzbekistan;1. Sunnybrook Research Institute, Evaluative Clinical Science, Toronto, Canada;2. University of Toronto, Department of Surgery, Toronto, Canada;3. Institute for Clinical Evaluative Sciences, Toronto, Canada;4. Pakistan Health Research Council, Islamabad, Pakistan;5. Hamad Medical Corporation, Department of Emergency Medicine, Doha, Qatar;6. Scarborough and Rouge Hospital, Toronto, Canada;7. University of Toronto, Department of Pediatrics, Toronto, Canada;1. IRCCS Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (I.R.S.T.), Meldola, Italy;2. Santa Maria della Misericordia Hospital, Udine, Italy;3. CROB, Rionero in Vulture, Italy;4. Santa Maria degli Angeli Hospital, Pordenone, Italy;5. Borgo Trento Hospital, Verona, Italy;6. University Hospital, Modena, Italy;7. Humanitas Hospital, Milano, Italy;8. Oncologia 2 – AOU Careggi, Florence, Italy;9. Versilia Hospital, Lido di Camaiore, Italy;10. IRCCS Azienda Ospedaliera Universitaria San Martino – IST Istituto Nazionale per la Ricerca sul Cancro, Genova, Italy;11. University Hospital, Ancona, Italy;12. Ca'' Foncello Hospital, Treviso, Italy;13. Spedali Civili, Brescia, Italy;14. Belcolle Hospital, Viterbo, Italy;15. Fazzi Hospital, Lecce, Italy;16. San Salvatore Hospital, Pesaro, Italy;17. Istituto Oncologico Veneto IOV-IRCCS, Padova, Italy;1. Unité de Recherche “Ecologie et Dynamique des Systèmes Anthropisés” EDYSAN, UMR 7058 CNRS-UPJV, Jules Verne University of Picardie, Amiens, France;2. Applied Plant Biotechnology Laboratory, Lebanese University, Faculty of Sciences, Life and Earth Sciences Department, Beirut, Lebanon
Abstract:Biological invasions are widely accepted as having a major impact on ecosystem functioning worldwide, giving urgency to a better understanding of the factors that control their spread. Modelling tools have been developed for this purpose but are often discrete-space, discrete-time spatial-mechanistic models that adopt a computer simulation approach and resist mathematical analysis. We constructed a simple demographic matrix model to explore the local population dynamics of an invasive species with a complex life history and whose invasive success depends on resource availability, which occurs stochastically. As a case study we focused on the American black cherry (Prunus serotina Ehrh.), a gap-dependent tree able both to constitute a long-living seedling bank under unfavourable light conditions and to resprout vigorously once cut-down, which is invading European temperate forests. The model used was a stage-classified matrix population model (i.e., Lefkovitch matrix), integrating environmental stochasticity. Stochastic matrix projection analysis was combined with elasticity analysis and stochastic simulations to search for the species’ ‘Achille heel’. As expected, the population growth rate (i.e., Lyapunov exponent), which measures the risk of P. serotina invasion at the stand scale, increased with light frequency. There was a critical value above which the population of P. serotina explodes and below which it locally goes extinct. The resprouting capacity usually speed up the invasion but appeared to play a minor role. The mean duration of stand invasion was measured and important life stage transitions that mostly contribute to the local stochastic growth rate were identified. Some relevant management implications are discussed and the interest of such models for the understanding of demographic characteristics of invasive species is stressed.
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